Long-range dependence in WiMAX downlink traffic

Stolojescu, Cristina; Moga, Sorin; Lenca, Philippe; Isar, Alexandru

doi:10.1109/isscs.2011.5978702

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…A wireless network traffic flow may present strong dependence among its samples with incidence of bursts at various scales and different scaling laws as found on wired network traffic flows .…”

Section: Multifractal Analysismentioning

confidence: 99%

Loss probability estimation for OFDM/TDMA‐based wireless systems considering a cascade‐based multifractal traffic model

Rocha

Vieira

2012

Trans. Emerging Tel. Tech.

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In this paper, we analyze the queueing performance in terms of byte loss rate of an orthogonal frequency‐division multiplexing/time division multiple access based wireless system taking into account the multifractal behaviour of the wireless traffic flows. To this end, first, we show evidences of multifractal characteristics on wireless traffic traces. These findings motivated us to derive a loss probability equation for wireless traffic flows that can be applied to an orthogonal frequency‐division multiplexing/time division multiple access based wireless system. Simulations and comparisons to other methods are carried out to verify the accuracy of the proposed loss probability estimation approach in describing real traffic queueing behaviour. Copyright © 2012 John Wiley & Sons, Ltd.

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Section: Multifractal Analysismentioning

confidence: 99%

Loss probability estimation for OFDM/TDMA‐based wireless systems considering a cascade‐based multifractal traffic model

Rocha

Vieira

2012

Trans. Emerging Tel. Tech.

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A novel hybrid prediction algorithm to network traffic

Jiang

2015

Ann. Telecommun.

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Network traffic describes the characteristics and users' behaviors of communication networks. It is a crucial input parameter of network management and network traffic engineering. This paper proposes a new prediction algorithm to network traffic in the large-scale communication network. First, we use signal analysis theory to transform network traffic from time domain to timefrequency domain. In the time-frequency domain, the network traffic signal is decomposed into the low-frequency and high-frequency components. Second, the gray model is exploited to model the low-frequency component of network traffic. The white Gaussian noise model is utilized to describe its high-frequency component. This is reasonable because the low-frequency and highfrequency components, respectively, represent the trend and fluctuation properties of network traffic, while the gray model and white Gaussian noise model can well capture the characteristics. Third, the prediction models of low-frequency and high-frequency components are built. The hybrid prediction algorithm is proposed to overcome the problem of network traffic prediction in the communication network. Finally, network traffic data from the real network is used to validate our approach. Simulation results indicate that our algorithm holds much lower prediction error than previous methods.

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